Save out dataparser transforms

nerfstudio/cameras/camera_utils.py

@@ -409,7 +409,7 @@ def rotation_matrix(a: TensorType[3], b: TensorType[3]) -> TensorType[3, 3]:
 
 def auto_orient_and_center_poses(
     poses: TensorType["num_poses":..., 4, 4], method: Literal["pca", "up", "none"] = "up", center_poses: bool = True
-) -> TensorType["num_poses":..., 3, 4]:
+) -> Tuple[TensorType["num_poses":..., 3, 4], TensorType[4, 4]]:
     """Orients and centers the poses. We provide two methods for orientation: pca and up.
 
     pca: Orient the poses so that the principal component of the points is aligned with the axes.
@@ -424,7 +424,7 @@ def auto_orient_and_center_poses(
         center_poses: If True, the poses are centered around the origin.
 
     Returns:
-        The oriented poses.
+        Tuple of the oriented poses and the transform matrix.
     """
 
     translation = poses[..., :3, 3]
@@ -457,7 +457,9 @@ def auto_orient_and_center_poses(
         transform = torch.cat([rotation, rotation @ -translation[..., None]], dim=-1)
         oriented_poses = transform @ poses
     elif method == "none":
-        oriented_poses = poses[:, :3]
-        oriented_poses[..., 3] -= translation
+        transform = torch.eye(4)
+        transform[:3, 3] = -translation
+        transform = transform[:3, :]
+        oriented_poses = transform @ poses
 
-    return oriented_poses
+    return oriented_poses, transform

nerfstudio/data/datamanagers/base_datamanager.py

@@ -35,6 +35,7 @@
 from nerfstudio.cameras.cameras import CameraType
 from nerfstudio.cameras.rays import RayBundle
 from nerfstudio.configs.base_config import InstantiateConfig
+from nerfstudio.data.dataparsers.base_dataparser import DataparserOutputs
 from nerfstudio.data.dataparsers.blender_dataparser import BlenderDataParserConfig
 from nerfstudio.data.dataparsers.dnerf_dataparser import DNeRFDataParserConfig
 from nerfstudio.data.dataparsers.friends_dataparser import FriendsDataParserConfig
@@ -46,7 +47,6 @@
 from nerfstudio.data.dataparsers.phototourism_dataparser import (
     PhototourismDataParserConfig,
 )
-from nerfstudio.data.dataparsers.record3d_dataparser import Record3DDataParserConfig
 from nerfstudio.data.datasets.base_dataset import InputDataset
 from nerfstudio.data.pixel_samplers import EquirectangularPixelSampler, PixelSampler
 from nerfstudio.data.utils.dataloaders import (
@@ -69,7 +69,6 @@
             "friends-data": FriendsDataParserConfig(),
             "instant-ngp-data": InstantNGPDataParserConfig(),
             "nuscenes-data": NuScenesDataParserConfig(),
-            "record3d-data": Record3DDataParserConfig(),
             "dnerf-data": DNeRFDataParserConfig(),
             "phototourism-data": PhototourismDataParserConfig(),
         },
@@ -296,6 +295,7 @@ class VanillaDataManager(DataManager):  # pylint: disable=abstract-method
     config: VanillaDataManagerConfig
     train_dataset: InputDataset
     eval_dataset: InputDataset
+    train_dataparser_outputs: DataparserOutputs
 
     def __init__(
         self,
@@ -321,8 +321,9 @@ def __init__(
 
     def create_train_dataset(self) -> InputDataset:
         """Sets up the data loaders for training"""
+        self.train_dataparser_outputs = self.dataparser.get_dataparser_outputs(split="train")
         return InputDataset(
-            dataparser_outputs=self.dataparser.get_dataparser_outputs(split="train"),
+            dataparser_outputs=self.train_dataparser_outputs,
             scale_factor=self.config.camera_res_scale_factor,
         )
 

nerfstudio/data/dataparsers/base_dataparser.py

@@ -16,6 +16,7 @@
 
 from __future__ import annotations
 
+import json
 from abc import abstractmethod
 from dataclasses import dataclass, field
 from pathlib import Path
@@ -63,11 +64,31 @@ class DataparserOutputs:
     """Dictionary of any metadata that be required for the given experiment.
     Will be processed by the InputDataset to create any additional tensors that may be required.
     """
+    dataparser_transform: TensorType[3, 4] = torch.eye(4)[:3, :]
+    """Transform applied by the dataparser."""
+    dataparser_scale: float = 1.0
+    """Scale applied by the dataparser."""
 
     def as_dict(self) -> dict:
         """Returns the dataclass as a dictionary."""
         return vars(self)
 
+    def save_dataparser_transform(self, path: Path):
+        """Save dataparser transform to json file. Some dataparsers will apply a transform to the poses,
+        this method allows the transform to be saved so that it can be used in other applications.
+
+        Args:
+            path: path to save transform to
+        """
+        data = {
+            "transform": self.dataparser_transform.tolist(),
+            "scale": self.dataparser_scale,
+        }
+        if not path.parent.exists():
+            path.parent.mkdir(parents=True)
+        with open(path, "w", encoding="UTF-8") as file:
+            json.dump(data, file, indent=4)
+
 
 @dataclass
 class DataParserConfig(cfg.InstantiateConfig):

nerfstudio/data/dataparsers/blender_dataparser.py

@@ -104,6 +104,7 @@ def _generate_dataparser_outputs(self, split="train"):
             cameras=cameras,
             alpha_color=alpha_color_tensor,
             scene_box=scene_box,
+            dataparser_scale=self.scale_factor,
         )
 
         return dataparser_outputs

nerfstudio/data/dataparsers/dnerf_dataparser.py

@@ -102,7 +102,11 @@ def _generate_dataparser_outputs(self, split="train"):
         )
 
         dataparser_outputs = DataparserOutputs(
-            image_filenames=image_filenames, cameras=cameras, alpha_color=alpha_color_tensor, scene_box=scene_box
+            image_filenames=image_filenames,
+            cameras=cameras,
+            alpha_color=alpha_color_tensor,
+            scene_box=scene_box,
+            dataparser_scale=self.scale_factor,
         )
 
         return dataparser_outputs

nerfstudio/data/dataparsers/friends_dataparser.py

@@ -146,5 +146,6 @@ def _generate_dataparser_outputs(self, split="train"):  # pylint: disable=unused
             cameras=cameras,
             scene_box=scene_box,
             metadata={"semantics": semantics} if self.config.include_semantics else {},
+            dataparser_scale=scale,
         )
         return dataparser_outputs

nerfstudio/data/dataparsers/instant_ngp_dataparser.py

@@ -115,6 +115,7 @@ def _generate_dataparser_outputs(self, split="train"):
             image_filenames=image_filenames,
             cameras=cameras,
             scene_box=scene_box,
+            dataparser_scale=self.config.scene_scale,
         )
 
         return dataparser_outputs

nerfstudio/data/dataparsers/nerfstudio_dataparser.py

@@ -181,7 +181,7 @@ def _generate_dataparser_outputs(self, split="train"):
             orientation_method = self.config.orientation_method
 
         poses = torch.from_numpy(np.array(poses).astype(np.float32))
-        poses = camera_utils.auto_orient_and_center_poses(
+        poses, transform_matrix = camera_utils.auto_orient_and_center_poses(
             poses,
             method=orientation_method,
             center_poses=self.config.center_poses,
@@ -190,9 +190,10 @@ def _generate_dataparser_outputs(self, split="train"):
         # Scale poses
         scale_factor = 1.0
         if self.config.auto_scale_poses:
-            scale_factor /= torch.max(torch.abs(poses[:, :3, 3]))
+            scale_factor /= float(torch.max(torch.abs(poses[:, :3, 3])))
+        scale_factor *= self.config.scale_factor
 
-        poses[:, :3, 3] *= scale_factor * self.config.scale_factor
+        poses[:, :3, 3] *= scale_factor
 
         # Choose image_filenames and poses based on split, but after auto orient and scaling the poses.
         image_filenames = [image_filenames[i] for i in indices]
@@ -252,6 +253,8 @@ def _generate_dataparser_outputs(self, split="train"):
             cameras=cameras,
             scene_box=scene_box,
             mask_filenames=mask_filenames if len(mask_filenames) > 0 else None,
+            dataparser_scale=scale_factor,
+            dataparser_transform=transform_matrix,
         )
         return dataparser_outputs
 

nerfstudio/data/dataparsers/phototourism_dataparser.py

@@ -137,16 +137,17 @@ def _generate_dataparser_outputs(self, split="train"):
         else:
             raise ValueError(f"Unknown dataparser split {split}")
 
-        poses = camera_utils.auto_orient_and_center_poses(
+        poses, transform_matrix = camera_utils.auto_orient_and_center_poses(
             poses, method=self.config.orientation_method, center_poses=self.config.center_poses
         )
 
         # Scale poses
         scale_factor = 1.0
         if self.config.auto_scale_poses:
-            scale_factor /= torch.max(torch.abs(poses[:, :3, 3]))
+            scale_factor /= float(torch.max(torch.abs(poses[:, :3, 3])))
+        scale_factor *= self.config.scale_factor
 
-        poses[:, :3, 3] *= scale_factor * self.config.scale_factor
+        poses[:, :3, 3] *= scale_factor
 
         # in x,y,z order
         # assumes that the scene is centered at the origin
@@ -175,6 +176,8 @@ def _generate_dataparser_outputs(self, split="train"):
             image_filenames=image_filenames,
             cameras=cameras,
             scene_box=scene_box,
+            dataparser_scale=scale_factor,
+            dataparser_transform=transform_matrix,
         )
 
         return dataparser_outputs

nerfstudio/engine/trainer.py

@@ -132,6 +132,10 @@ def train(self) -> None:
         """Train the model."""
         assert self.pipeline.datamanager.train_dataset is not None, "Missing DatsetInputs"
 
+        self.pipeline.datamanager.train_dataparser_outputs.save_dataparser_transform(
+            self.base_dir / "dataparser_transforms.json"
+        )
+
         self._init_viewer_state()
         with TimeWriter(writer, EventName.TOTAL_TRAIN_TIME):
             num_iterations = self.config.trainer.max_num_iterations